How to calculate confidence score in object detection?
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| Papers (15) | Insight |
|---|---|
01 Jan 2010 21 Citations | An important property of our algorithm is that the object detector module is capable of adaptively changing its confidence in establishing whether a certain region of interest contains an object (or not) as new evidence is gathered about the scene layout. |
01 Dec 2010 | The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods. |
62 Citations | The obtained results show that, by modelling detection uncertainty, a confidence level can be associated to each detected object, making the defect detection more precise and reliable. |
19 Aug 2016 40 Citations | In this paper, we develop a high-confidence region-based object detection framework that boosts up the classification performance with less computational burden. |
07 Oct 2012 | By introducing a second target classifier and using the ranking score from the pre-trained classifier as the detection confidence measure, we add additional robustness against unreliable detections. |
| Then, a confidence interval calculation helps the pixels' classification to speed up the detection processing. | |
01 Nov 2018 74 Citations | Established algorithms for estimating uncertainty can either not be directly applied to object detection networks or result in high inference times. |
| Thus, there is a requirement to improve the confidence measure regarding target and non-target object. | |
21 Jul 2017 | In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. |
| This confidence measure was implemented and found to improve accuracy on two object detection problems: face detection and fish detection. | |
28 Jun 2009 | The confidence estimates provided by the present algorithm allow on-line control of the detection and tracking quality. |
01 Jun 2018 | Furthermore, since our method is based on scores and bounding boxes without modification on the architecture of object detector, it can be easily applied to off-the-shelf modern object detection frameworks. |
74 Citations | Experiments show that the mean average precision of object detection is improved after the addition of MSCA to the current object detection model. |
| Experimental results show that the model can achieve high accuracy in predicting the performance of object detection. | |
| This new method provides a means of reliable estimation of the confidence intervals for the detection hypothesis. |
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How does signal detection theory apply to a confidence measure recognition test?5 answersSignal detection theory (SDT) is a popular framework for analyzing data from studies of human behavior, including investigations of confidence. SDT-based analyses of confidence provide estimates of sensitivity (d') and a second estimate based on high-confidence decisions (meta d'). The difference between meta d' and d' is considered a measure of metacognitive inefficiency, indicating the contamination of confidence by additional noise. However, these analyses rely on the assumption that repeated exposures to an input will evoke a normally-shaped distribution of perceptual experiences. Recent research has shown that when distributions of experiences deviate from this assumption, meta d' can be systematically underestimated relative to d'. Therefore, SDT-based analyses of confidence may not provide a ground truth measure of human metacognitive inefficiency.
How Mean Average Precision used in object detection?3 answersMean Average Precision (mAP) is a standard score used to evaluate object detectors in the field of computer vision. It quantifies the accuracy of object detection algorithms by measuring the precision and recall of the detected objects. The mAP score is sensitive to small perturbations in the bounding box predictions, such as translations and scaling. For example, a one-pixel shift in the bounding box can cause an 8.4% drop in mAP. This sensitivity to perturbations explains why achieving higher mAP becomes increasingly difficult as models improve. In the context of autonomous driving, sensor fusion using camera and radar data has gained popularity for object detection. Radar data augmentation techniques have been explored to improve the performance of object detection models, resulting in higher mAP scores compared to baseline results.
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